code: def create(
      observation_data,
      user_id="user_id",
      item_id="item_id",
      target=None,
      user_data=None,
      item_data=None,
      random_seed=0,
      verbose=True,
  ):
      """
      Create a model that makes recommendations using item popularity. When no
      target column is provided, the popularity is determined by the number of
      observations involving each item. When a target is provided, popularity
      is computed using the item's mean target value. When the target column
      contains ratings, for example, the model computes the mean rating for
      each item and uses this to rank items for recommendations.
  
      Parameters
      ----------
      observation_data : SFrame
          The dataset to use for training the model. It must contain a column of
          user ids and a column of item ids. Each row represents an observed
          interaction between the user and the item.  The (user, item) pairs
          are stored with the model so that they can later be excluded from
          recommendations if desired. It can optionally contain a target ratings
          column. All other columns are interpreted by the underlying model as
          side features for the observations.
  
          The user id and item id columns must be of type 'int' or 'str'. The
          target column must be of type 'int' or 'float'.
  
      user_id : string, optional
          The name of the column in `observation_data` that corresponds to the
          user id.
  
      item_id : string, optional
          The name of the column in `observation_data` that corresponds to the
          item id.
  
      target : string, optional
          The `observation_data` can optionally contain a column of scores
          representing ratings given by the users. If present, the name of this
          column may be specified variables `target`.
  
      user_data : SFrame, optional
          Side information for the users.  This SFrame must have a column with
          the same name as what is specified by the `user_id` input parameter.
          `user_data` can provide any amount of additional user-specific
          information.
  
      item_data : SFrame, optional
          Side information for the items.  This SFrame must have a column with
          the same name as what is specified by the `item_id` input parameter.
          `item_data` can provide any amount of additional item-specific
          information.
  
      verbose : bool, optional
          Enables verbose output.
  
      Examples
      --------
      >>> sf = turicreate.SFrame({'user_id': ["0", "0", "0", "1", "1", "2", "2", "2"],
      ...                       'item_id': ["a", "b", "c", "a", "b", "b", "c", "d"],
      ...                       'rating': [1, 3, 2, 5, 4, 1, 4, 3]})
      >>> m = turicreate.popularity_recommender.create(sf, target='rating')
  
      See Also
      --------
      PopularityRecommender
      """
      from turicreate._cython.cy_server import QuietProgress
  
      if not (isinstance(observation_data, _SFrame)):
          raise TypeError("observation_data input must be a SFrame")
      opts = {}
      model_proxy = _turicreate.extensions.popularity()
      model_proxy.init_options(opts)
  
      if user_data is None:
          user_data = _turicreate.SFrame()
      if item_data is None:
          item_data = _turicreate.SFrame()
  
      opts = {
          "user_id": user_id,
          "item_id": item_id,
          "target": target,
          "random_seed": 1,
      }
  
      extra_data = {"nearest_items": _turicreate.SFrame()}
      with QuietProgress(verbose):
          model_proxy.train(
              observation_data, user_data, item_data, opts, extra_data
          )
  
      return PopularityRecommender(model_proxy)
  
file_path: turicreate/src/python/turicreate/toolkits/recommender/popularity_recommender.py
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits.recommender.util import _Recommender
  from turicreate.data_structures.sframe import SFrame
label_desc: Use image node_id 1 for calling a global function with example usage: create(observation_data, user_id, item_id, target, user_data, item_data, random_seed, verbose) and returns: PopularityRecommender